Stochastic Prediction in Multinomial Logit Models Export

@article{citeulike:1109358, abstract = {It is standard practice to form predictions from multinomial logit models by ignoring the estimation error associated with the parameter estimates and solving for the predicted endogeneous variable (market share) in terms of the exogenous variables and the point estimates of the parameters. It has long been recognized in the econometrics literature that this type of nonstochastic prediction, which ignores the sampling distribution of the parameter estimates, leads to incorrect inferences about the endogenous variable. We offer a simulation-based approach for approximating the exact stochastic prediction. We show that this approach provides very accurate approximations with minimal computation time and would be easy to implement in industrial applications.}, author = {Hsu, Arthur and Wilcox, Ronald T.}, citeulike-article-id = {1109358}, citeulike-linkout-0 = {http://www.jstor.org/stable/2661589}, journal = {Management Science}, keywords = {bryan}, number = {8}, pages = {1137--1144}, posted-at = {2007-02-16 05:53:27}, priority = {2}, title = {Stochastic Prediction in Multinomial Logit Models}, url = {http://www.jstor.org/stable/2661589}, volume = {46}, year = {2000} }

TY - JOUR ID - citeulike:1109358 L3 - citeulike-article-id:1109358 N2 - It is standard practice to form predictions from multinomial logit models by ignoring the estimation error associated with the parameter estimates and solving for the predicted endogeneous variable (market share) in terms of the exogenous variables and the point estimates of the parameters. It has long been recognized in the econometrics literature that this type of nonstochastic prediction, which ignores the sampling distribution of the parameter estimates, leads to incorrect inferences about the endogenous variable. We offer a simulation-based approach for approximating the exact stochastic prediction. We show that this approach provides very accurate approximations with minimal computation time and would be easy to implement in industrial applications. IS - 8 JF - Management Science EP - 1144 TI - Stochastic Prediction in Multinomial Logit Models VL - 46 SP - 1137 KW - bryan AU - Hsu, Arthur AU - Wilcox, Ronald PY - 2000/// UR - http://www.jstor.org/stable/2661589 ER -